LAMOST J040901.83+329355.6 – a new Galactic star with Wolf–Rayet characteristics in the transitional stage from post-asymptotic giant branch to central star of a planetary nebula
Bibliographic record
Abstract
ABSTRACT The similarity in physical conditions in the winds of low-mass post-asymptotic giant branch stars and evolved massive stars leads to the appearance of an interesting phenomenon of spectral mimicry. Because of this, the discovery of every new star with a Wolf–Rayet (WR) spectrum requires a special study of its evolutionary status before it can be included in the list of Galactic WR stars. A couple of years ago, LAMOST J040901.83+323955.6 (hereafter J0409+3239) was selected as a WR star in the LAMOST spectroscopic data base by machine-learning methods. In this work, we investigate its evolutionary status. After analysing the spatial location of J0409+3239 in the Galaxy and its position in the colour–magnitude diagram, we conclude that J0409+3239 is instead a low-mass object displayng the WR phenomenon. Its luminosity is $L*=1000~\rm L_\odot$ and its effective temperature is Teff = 40 000 K. Using new and archival photometric data, we detected irregular variability on time-scales from hours to tens of days with an amplitude of up to ~0.2 mag. A comparison of the spectrum obtained in 2022 with that from 2014 also shows evidence of spectral variability. The absence of a clearly detected circumstellar nebula prevents the classification of J0409+3239 as [WR], namely as the central star of a planetary nebula (CSPN). However, the position of J0409+3239 on the Hertzsprung–Russell diagram suggests that this object is a low-mass star caught in a rare transitional phase to CSPN. Estimation of the J0409+3239 mass based on evolutionary tracks shows that it is less than $0.9~\rm M_\odot$, and thus that the age of the Galaxy is barely sufficient for the star to have evolved to its current stage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".